周末是最好的学习时间
Weekends are the best time for studying🙇🏻
【对罗福莉的3.5小时访谈:AI范式已然巨变!OpenClaw、智能体框架、Agent范式很吃Post-train、卡的分配比例、巨变下的组织-哔哩哔哩】 https://t.co/WIaLFTkYrP
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📹 Videos:
1. LLM Introduction: https://t.co/kJDquHyQuR
2. LLMs from Scratch: https://t.co/0tVKf67LWE
3. Agentic AI Overview (Stanford): https://t.co/F3eMqlyx7o
4. Building and Evaluating Agents: https://t.co/p2wAwQkmc1
5. Building Effective Agents: https://t.co/soZEzoU6eu
6. Building Agents with MCP: https://t.co/7rXLH619p4
7. Building an Agent from Scratch: https://t.co/JVVEvlwcvH
8. Philo Agents: https://t.co/oALtKeEhg1
🗂️ Repos
1. GenAI Agents: https://t.co/SzAvw64ZA3
2. Microsoft's AI Agents for Beginners: https://t.co/MYCOwStucr
3. Prompt Engineering Guide: https://t.co/zFZJT6V60r
4. Hands-On Large Language Models: https://t.co/S5E4390RIk
5. AI Agents for Beginners: https://t.co/MYCOwStucr
6. GenAI Agentshttps://lnkd.in/dEt72MEy
7. Made with ML: https://t.co/mAb4b9Li9o
8. Hands-On AI Engineering:https://t.co/2QvXB3WJhe
9. Awesome Generative AI Guide: https://t.co/dYaAsRgfO6
10. Designing Machine Learning Systems: https://t.co/jRxshvMgJt
11. Machine Learning for Beginners from Microsoft: https://t.co/6u48FQng1g
12. LLM Course: https://t.co/o0NnbEjH6X
🗺️ Guides
1. Google's Agent Whitepaper: https://t.co/cs0P2Tt165
2. Google's Agent Companion: https://t.co/Qnv3PsJZIx
3. Building Effective Agents by Anthropic: https://t.co/5ZfcMllO9N.
4. Claude Code Best Agentic Coding practices: https://t.co/zX9ep8ER0h
5. OpenAI's Practical Guide to Building Agents: https://t.co/uwdBKet060
📚Books:
1. Understanding Deep Learning: https://t.co/Rix5N440Y8
2. Building an LLM from Scratch: https://t.co/V20ES23ZH8
3. The LLM Engineering Handbook: https://t.co/avpqPTA0I8
4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/8bgDLtebU0
5. Building Applications with AI Agents - Michael Albada: https://t.co/W70co41CCW
6. AI Agents with MCP - Kyle Stratis: https://t.co/vF8VqTeyfA
7. AI Engineering: https://t.co/eJrAoLMW0Z
📜 Papers
1. ReAct: https://t.co/SFgUispJcP
2. Generative Agents: https://t.co/q50bu1PPnQ.
3. Toolformer: https://t.co/CFssbdAXvQ
4. Chain-of-Thought Prompting: https://t.co/n84jvdyxWL.
🧑🏫 Courses:
1. HuggingFace's Agent Course: https://t.co/yhVP0jcs6w
2. MCP with Anthropic: https://t.co/w9LxesXtjx
3. Building Vector Databases with Pinecone: https://t.co/GeI4yarzHH
4. Vector Databases from Embeddings to Apps: https://t.co/eMrFYZaY8d
5. Agent Memory: https://t.co/hjbH72Qwqr
Repost for your network ♻️
很多经验丰富的程序员,在面对复杂软件系统时游刃有余,但一碰到机器学习,满屏的数学公式,让人难以建立直观的工程直觉。
最近在 GitHub 上看到 There Is No Spoon 这份开源教程,专为工程师量身定制的机器学习入门指南。
抛弃了枯燥的教科书式说教,巧妙地将抽象的 ML 概念全部转化为我们熟悉的物理和工程类比。
把神经元比作偏振镜,把模型深度比作折纸,用最直白的方式帮我们重塑对算法的底层认知。
GitHub:https://t.co/QFdHy04J6O
内容覆盖三大部分:基础概念、主流架构(卷积、注意力、循环网络、状态空间模型等),以及门控机制作为控制系统的实践工具箱。
重点不在于死记硬背数学推导,而是教我们面对具体问题时该如何选择工具,以及做出架构决策背后的权衡思考。
整份教程是一个 Markdown 文件,配有 12 张可视化插图,建议搭配 AI 助手一起读,把教程喂给它然后逐节对话探索,效果更好。
如果你有代码编程基础,又想快速掌握机器学习,这份教程值得收藏从头开始学习。
We just released Gemma 4 — our most intelligent open models to date.
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